Generative Pretraining for Black-Box Optimization

• URL: http://arxiv.org/abs/2206.10786v4
• Date: Mon, 21 Aug 2023 16:49:14 GMT
• Title: Generative Pretraining for Black-Box Optimization
• Authors: Siddarth Krishnamoorthy, Satvik Mehul Mashkaria, Aditya Grover
• Abstract summary: We propose BONET, a generative framework for pretraining a novel black-box function. In BONET, we train an autoregressive model on fixed-length trajectories derived from an offline dataset. We rank BONET on Design-Bench, where we rank the best on average, outperforming state-of-the-art baselines.
• Score: 29.64357898080842
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Many problems in science and engineering involve optimizing an expensive black-box function over a high-dimensional space. For such black-box optimization (BBO) problems, we typically assume a small budget for online function evaluations, but also often have access to a fixed, offline dataset for pretraining. Prior approaches seek to utilize the offline data to approximate the function or its inverse but are not sufficiently accurate far from the data distribution. We propose BONET, a generative framework for pretraining a novel black-box optimizer using offline datasets. In BONET, we train an autoregressive model on fixed-length trajectories derived from an offline dataset. We design a sampling strategy to synthesize trajectories from offline data using a simple heuristic of rolling out monotonic transitions from low-fidelity to high-fidelity samples. Empirically, we instantiate BONET using a causally masked Transformer and evaluate it on Design-Bench, where we rank the best on average, outperforming state-of-the-art baselines.

Related papers

• Guided Trajectory Generation with Diffusion Models for Offline Model-based Optimization [19.228156994045587]
  We introduce a novel conditional generative modeling approach to produce trajectories toward high-scoring regions. Experiment results demonstrate that our method outperforms competitive baselines on Design-Bench.
  arXiv  Detail & Related papers  (2024-06-29T06:12:36Z)
• ROMO: Retrieval-enhanced Offline Model-based Optimization [14.277672372460785]
  Data-driven black-box model-based optimization (MBO) problems arise in a number of practical application scenarios. We propose retrieval-enhanced offline model-based optimization (ROMO) ROMO is simple to implement and outperforms state-of-the-art approaches in the CoMBO setting.
  arXiv  Detail & Related papers  (2023-10-11T15:04:33Z)
• Diffusion Models for Black-Box Optimization [29.64357898080842]
  We propose Denoising Diffusion Optimization Models (DDOM) for offline black-box optimization. Given an offline dataset, DDOM learns a conditional generative model over the domain of the black-box function conditioned on the function values. We show DDOM achieves results competitive with state-of-the-art baselines.
  arXiv  Detail & Related papers  (2023-06-12T15:26:44Z)
• Prior-mean-assisted Bayesian optimization application on FRIB Front-End tunning [61.78406085010957]
  We exploit a neural network model trained over historical data as a prior mean of BO for FRIB Front-End tuning. In this paper, we exploit a neural network model trained over historical data as a prior mean of BO for FRIB Front-End tuning.
  arXiv  Detail & Related papers  (2022-11-11T18:34:15Z)
• Pre-training helps Bayesian optimization too [49.28382118032923]
  We seek an alternative practice for setting functional priors. In particular, we consider the scenario where we have data from similar functions that allow us to pre-train a tighter distribution a priori. Our results show that our method is able to locate good hyper parameters at least 3 times more efficiently than the best competing methods.
  arXiv  Detail & Related papers  (2022-07-07T04:42:54Z)
• Design-Bench: Benchmarks for Data-Driven Offline Model-Based Optimization [82.02008764719896]
  Black-box model-based optimization problems are ubiquitous in a wide range of domains, such as the design of proteins, DNA sequences, aircraft, and robots. We present Design-Bench, a benchmark for offline MBO with a unified evaluation protocol and reference implementations of recent methods. Our benchmark includes a suite of diverse and realistic tasks derived from real-world optimization problems in biology, materials science, and robotics.
  arXiv  Detail & Related papers  (2022-02-17T05:33:27Z)
• Conservative Objective Models for Effective Offline Model-Based Optimization [78.19085445065845]
  Computational design problems arise in a number of settings, from synthetic biology to computer architectures. We propose a method that learns a model of the objective function that lower bounds the actual value of the ground-truth objective on out-of-distribution inputs. COMs are simple to implement and outperform a number of existing methods on a wide range of MBO problems.
  arXiv  Detail & Related papers  (2021-07-14T17:55:28Z)
• JUMBO: Scalable Multi-task Bayesian Optimization using Offline Data [86.8949732640035]
  We propose JUMBO, an MBO algorithm that sidesteps limitations by querying additional data. We show that it achieves no-regret under conditions analogous to GP-UCB. Empirically, we demonstrate significant performance improvements over existing approaches on two real-world optimization problems.
  arXiv  Detail & Related papers  (2021-06-02T05:03:38Z)
• Projection & Probability-Driven Black-Box Attack [205.9923346080908]
  Existing black-box attacks suffer from the need for excessive queries in the high-dimensional space. We propose Projection & Probability-driven Black-box Attack (PPBA) to tackle this problem. Our method requires at most 24% fewer queries with a higher attack success rate compared with state-of-the-art approaches.
  arXiv  Detail & Related papers  (2020-05-08T03:37:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.